The degradation mode of the noisy image is usually formulated as \(y = x + v\) , where \(x\)  is the clean image, \(v\)  is the additive white Gaussian noise (AWGN), and \(y\)  is noisy image polluted by AWGN.
Recently, many excellent methods have been proposed for clear images estimation, including spatial filtering methods [1]}, [2]}, model-based methods [3]}, [4]}, and deep learning-based methods [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
Among them, deep learning-based methods have become the mainstream of SID, which aim to solve the problem via learning the mapping between the noisy and clean images.
For example, Zhang et al. [5]} proposed a DnCNN for the Gaussian noise removal, which achieved competitive results by took advantage of batch normalization and residual learning.
Yu et al. [15]} proposed a Deep Iterative Down-up Network (DIDN) for image denoising, which achieved promising results.
However, directly minimizing the loss function is difficult to learn accurate mapping.
To solve this problem, Fang et al. [9]} introduced the edge priors to guide image denoising and proposed a Multi-level Edge Features Guided Network (MLEFGN).
However, the edge prior still is an artificially designed image prior and it is only one of many image priors.
This certain extent limits its flexibility and versatility so that it can only improve the local effects in the image.
Different from it, we aim to explore a method that allows the model to automatically learn the deep priors according to the need of the image, and to build a more efficient and lightweight SID model.
<FIGURE>